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Very Short/Short/Medium/Long Term Load Forecasting and Renewables Forecasting

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 17538

Special Issue Editor


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Guest Editor
Department of Agricultural and Forestry Engineering, University of Valladolid, Campus Duques de Soria, 42004 Soria, Spain
Interests: energy; engineering; computer science; photovoltaic systems; microgrids; distributed generation; smart metering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The electrical system needs an adjustment between generation and demand. This adjustment is possible thanks to storage, but to manage the system, it needs forecasting tools, both for demand and generation. Electricity demand is highly variable and dependent on certain exogenous and endogenous parameters. The forecasting models are different according to their forecasting horizon (very short, short, medium, and long term), the model used, and other characteristics. Renewable generation (mainly wind and solar) is highly variable since it depends on the resource. Having forecast models for the resource and generation is important for the managers of the electricity system.

We encourage authors to submit research papers, reviews, technical papers, case studies, and methodologies. With the topic "Very Short/Short/ Medium/Long-Term Load Forecasting and Renewables Forecasting", the SI receives articles on:

  • Statistical forecasting models (ARIMA; SARIMA; ARMAX; multi-variate regression; Kalman filter; etc.);
  • Artificial neural networks (ANNs);
  • Knowledge-based expert systems and fuzzy theory and fuzzy inference systems;
  • Evolutionary computation models and evolutionary algorithms;
  • Support vector regression (SVR);
  • Chaos theory;
  • New models for forecasting demand and generation (mainly renewable);
  • Hybrid forecast architectures (demand and generation);
  • Important variables in the forecast;
  • Correlation analysis between variables;
  • Forecasting applications to the management of the electrical system or microgrids;
  • Forecast (renewable demand or generation) for the management of storage in Smart Grids or Microgrids.

Prof. Dr. Luis Hernández-Callejo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

24 pages, 5316 KiB  
Article
Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
by Israt Fatema, Gang Lei and Xiaoying Kong
Appl. Sci. 2023, 13(11), 6520; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116520 - 26 May 2023
Cited by 3 | Viewed by 1231
Abstract
Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage [...] Read more.
Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making. Full article
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21 pages, 4096 KiB  
Article
A Group Resident Daily Load Forecasting Method Fusing Self-Attention Mechanism Based on Load Clustering
by Jie Cao, Ru-Xuan Zhang, Chao-Qiang Liu, Yuan-Bo Yang and Chin-Ling Chen
Appl. Sci. 2023, 13(2), 1165; https://0-doi-org.brum.beds.ac.uk/10.3390/app13021165 - 15 Jan 2023
Viewed by 1311
Abstract
Daily load forecasting is the basis of the economic and safe operation of a power grid. Accurate prediction results can improve the matching of microgrid energy storage capacity allocation. With the popularization of smart meters, the interaction between residential electricity demand and sources [...] Read more.
Daily load forecasting is the basis of the economic and safe operation of a power grid. Accurate prediction results can improve the matching of microgrid energy storage capacity allocation. With the popularization of smart meters, the interaction between residential electricity demand and sources and networks is increasing, and massive data are generated at the same time. Previous forecasting methods suffer from poor targeting and high noise. They cannot make full use of the important information of the load data. This paper proposes a new framework for daily load forecasting of group residents. Firstly, we use the singular value decomposition to address the problem of high dimensions of residential electricity data. Meanwhile, we apply a K-Shape-based group residential load clustering method to obtain the typical residential load data. Secondly, we introduce an empirical mode decomposition method to address the problem of high noise of residential load data. Finally, we propose a Bi-LSTM-Attention model for residential daily load forecasting. This method can make full use of the contextual information and the important information of the daily load of group residents. The experiments conducted on a real data set of a power grid show that our method achieves excellent improvements on five prediction error indicators, such as MAPE, which are significantly smaller than the compared baseline methods. Full article
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18 pages, 1753 KiB  
Article
A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models
by Rabin Dhakal, Ashish Sedai, Suhas Pol, Siva Parameswaran, Ali Nejat and Hanna Moussa
Appl. Sci. 2022, 12(18), 9038; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189038 - 8 Sep 2022
Cited by 14 | Viewed by 2069
Abstract
The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated [...] Read more.
The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model. Full article
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16 pages, 3942 KiB  
Article
Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network
by Changchun Cai, Yuanjia Li, Zhenghua Su, Tianqi Zhu and Yaoyao He
Appl. Sci. 2022, 12(13), 6647; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136647 - 30 Jun 2022
Cited by 40 | Viewed by 2632
Abstract
With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) [...] Read more.
With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) and time convolutional network (TCN) into a hybrid network is proposed in this paper. Firstly, original electrical load sequence data with noise are decomposed into intrinsic IMF components with different frequencies and amplitudes based on the VMD method. Secondly, a combined load forecasting method based on the GRU and TCN network is proposed for the high and low-frequency load subsequent signals, respectively. Finally, the high and low-frequency signals forecasting results of the GRU and TCN network are rebuilt for the final load forecasting. The experiment results based on actual operation data (data set 1) and simulation data (data set 2), which show that the proposed method can reduce the forecasting error by 36.20% and 10.8%, respectively, in comparison with VMD-GRU. The reliability and accuracy of the proposed method is verified through the comparison with other methods such as LSTM, Prophet and XG Boost. Full article
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23 pages, 5065 KiB  
Article
Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling
by Rehman Zafar, Ba Hau Vu, Munir Husein and Il-Yop Chung
Appl. Sci. 2021, 11(15), 6738; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156738 - 22 Jul 2021
Cited by 17 | Viewed by 3004
Abstract
At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. [...] Read more.
At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model. Full article
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17 pages, 3550 KiB  
Article
Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
by Tzu-Lun Yuan, Dian-Sheng Jiang, Shih-Yun Huang, Yuan-Yu Hsu, Hung-Chih Yeh, Mong-Na Lo Huang and Chan-Nan Lu
Appl. Sci. 2021, 11(13), 5930; https://0-doi-org.brum.beds.ac.uk/10.3390/app11135930 - 25 Jun 2021
Cited by 6 | Viewed by 2134
Abstract
Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce [...] Read more.
Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications. Full article
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29 pages, 8134 KiB  
Article
Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
by Manuel Lopez-Martin, Antonio Sanchez-Esguevillas, Luis Hernandez-Callejo, Juan Ignacio Arribas and Belen Carro
Appl. Sci. 2021, 11(12), 5708; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125708 - 20 Jun 2021
Cited by 18 | Viewed by 2646
Abstract
This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different classic machine learning and deep learning techniques [...] Read more.
This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different classic machine learning and deep learning techniques and recent methods for data-driven analysis of dynamical models (dynamic mode decomposition) and deep learning ensemble models applied to short-term load forecasting. This work explores the influence of critical parameters when performing time-series forecasting, such as rolling window length, k-step ahead forecast length, and number/nature of features used to characterize the information used as predictors. The deep learning architectures considered include 1D/2D convolutional and recurrent neural networks and their combination, Seq2seq with and without attention mechanisms, and recent ensemble models based on gradient boosting principles. Three groups of models stand out from the rest according to the forecast scenario: (a) deep learning ensemble models for average results, (b) simple linear regression and Seq2seq models for very short-term forecasts, and (c) combinations of convolutional/recurrent models and deep learning ensemble models for longer-term forecasts. Full article
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